Create an Advanced Python AI Agent with RAG
Table of Contents
- Introduction
- Building an Artificial Intelligence Agent
- Retrieval Augmented Generation (RAG)
- Utilizing Different Data Sources
- Population Data CSV File
- PDF Data Specific to Canada
- The Power of Notes
- Extending the Functionality
- Working with Llama Index
- Setting Up the Environment
- Creating the Virtual Environment
- Installing Required Python Packages
- Downloading Data Sources
- Accessing OpenAI API Key
- Getting Started with Pandas
- Querying over Pandas Data
- Incorporating the Note Engine
- Creating a Simple While Loop
- Adding the PDF Data Reader
- Building the Vector Store Index
- Using the Canada Engine
- Final Remarks
🤖 Building an Artificial Intelligence Agent
Artificial intelligence has rapidly advanced in recent years, allowing us to create agents with the ability to make decisions and solve problems autonomously. In this article, we will explore how to build an artificial intelligence agent that can utilize a variety of tools to assist us in our tasks. Even if you're a beginner or intermediate programmer, you'll be able to follow along and learn how to create your own agent.
Retrieval Augmented Generation (RAG)
One of the key components of our AI agent is the ability to use retrieval augmented generation (RAG). With RAG, we can provide additional data to the model so that it can reason based on that data rather than relying solely on its training data. In this case, we have two data sources: a population data CSV file and a PDF file specific to Canada. By incorporating RAG, our agent can switch between using these data sources to provide us with the most accurate and up-to-date information.
Utilizing Different Data Sources
To demonstrate the capabilities of our AI agent, we will be working with two different data sources: a population data CSV file and a PDF file specific to Canada. The population data CSV file contains information about population density, changes, and other demographic data. The PDF file provides more detailed information about Canada, such as languages spoken and other country-specific details. Our agent will be able to switch between these data sources based on the queries it receives.
Population Data CSV File
The population data CSV file is a structured data source that our agent can easily ingest and read. It contains comprehensive information about population density, changes, and other demographic data. Our agent can answer questions based on this data and provide accurate population statistics for different countries. This data source serves as a reliable and rich resource for our AI agent.
PDF Data Specific to Canada
In addition to the population data CSV file, we also have a PDF file specifically about Canada. While this is just the Wikipedia page for Canada, it provides additional context and information that our agent can use to answer specific questions. The agent can switch to this data source when queries are specific to Canada, enabling it to provide more detailed and accurate responses. This demonstrates the flexibility and adaptability of our AI agent.
The Power of Notes
Our AI agent also has the capability to take notes. At any point in time, we can ask the agent to save a note, and it will store that information for us. This feature is simple yet powerful, allowing us to quickly capture important details or reminders. The agent can store these notes in a separate file, allowing us to easily refer back to them. This functionality opens up a world of possibilities, as we can give the agent access to various tools and APIs to perform advanced tasks based on our needs.
Extending the Functionality
The capabilities of our AI agent extend beyond what we have demonstrated so far. We can instruct the agent to call an API, perform complex calculations, or execute any Python function of our choice. By providing the agent with additional tools and functionalities, we can customize its behavior and tailor it to our specific requirements. The flexibility and scalability of our AI agent make it a valuable asset for various applications.
Working with Llama Index
To build our AI agent, we have partnered with Llama Index. Llama Index provides an open-source Package that allows us to ingest and index different types of data, both structured and unstructured. By leveraging Llama Index, we can easily read in and query various data sources, making it simpler for our AI agent to access and analyze information. Llama Index offers a wide range of tools and capabilities, making it a powerful resource for our AI project.
Setting Up the Environment
Before we start building our AI agent, we need to set up our development environment. We will create a virtual environment and install the necessary Python packages. Additionally, we will download the required data sources and obtain an API key from OpenAI to access their models. These preparatory steps are essential for ensuring that our environment is ready for development.
Creating the Virtual Environment
To create the virtual environment, we will use Python's built-in venv module. This allows us to isolate our project dependencies and keep them separate from our system-wide Python installation. By creating a virtual environment, we can ensure that our project's packages and dependencies are contained within this environment, preventing conflicts with other Python projects or packages.
Installing Required Python Packages
Within our virtual environment, we need to install several Python packages that are essential for building our AI agent. These packages include Llama Index, PyPDF, and Pandas. Llama Index provides the tools and functionality to ingest and query different data sources. PyPDF allows us to read and extract information from PDF files. Pandas is a popular data science library that we will use to handle and analyze structured data.
Downloading Data Sources
As part of our AI project, we need to download two data sources: the population data CSV file and the PDF file specific to Canada. The population data CSV file contains comprehensive information about population density and demographic data for different countries. The PDF file provides more detailed information about Canada, including language statistics and other country-specific details. By downloading these data sources, we can incorporate them into our AI agent and provide accurate information based on the queries we receive.
Accessing OpenAI API Key
To utilize the advanced capabilities of OpenAI models, we need to obtain an API key. OpenAI provides powerful language models that can be used for various tasks, including question answering and natural language processing. By accessing the OpenAI API, we can integrate these models into our AI agent and enhance its functionality. The API key allows us to authenticate and access these models securely.
Getting Started with Pandas
Pandas is a versatile Python library that we will use to read in and manipulate structured data. In our case, we will use Pandas to read in the population data CSV file and perform queries on the data. Pandas provides a comprehensive set of tools for data analysis, making it easy to filter, sort, and manipulate data frames. By leveraging Pandas, we can effectively work with structured data sources and extract Meaningful insights.
Querying over Pandas Data
Once we have loaded our structured data into Pandas data frames, we can perform queries and retrieve specific information based on our requirements. Pandas provides a powerful querying interface that allows us to filter and aggregate data effortlessly. With our AI agent, we can leverage this capability to retrieve population statistics, demographic data, and other Relevant information. Our agent can handle complex queries and deliver accurate responses based on the data it has ingested.
Incorporating the Note Engine
The note engine is another tool that our AI agent can utilize. It allows us to save notes and store them for future reference. This simple yet effective functionality enables us to capture important information or reminders during interactions with the agent. By incorporating the note engine, we can extend the capabilities of our AI agent and make it a valuable personal assistant. The agent can seamlessly switch between different tools and perform actions based on our instructions.
Creating a Simple While Loop
To provide a user-friendly interaction with our AI agent, we will create a simple while loop. This loop will continuously Prompt the user for input and process their queries using the agent. By utilizing a while loop, we can create an interactive and conversational experience. The loop will only exit when the user enters a specific command, such as "Q" to quit. This ensures that our AI agent remains available and responsive for as long as we desire.
Adding the PDF Data Reader
Reading unstructured data, such as PDF files, requires specialized readers. In our case, we will use the PDF reader provided by Llama Index. This allows us to extract text and information from PDF files and incorporate them into our AI agent. By utilizing the PDF reader, we can access unstructured data sources and retrieve relevant information for different queries. This demonstrates the versatility and flexibility of our AI agent.
Building the Vector Store Index
To effectively query and retrieve information from unstructured data, we need to create a vector store index. This index enables quick and efficient searching of the data by converting it into embeddings, or multi-dimensional objects. By leveraging the vector store index, our AI agent can find relevant information based on WORD similarity and provide accurate responses to queries. The creation of the index involves the conversion of data into embeddings and the storage of the index for future use.
Using the Canada Engine
With the vector store index in place, we can now create a query engine for our Canada-specific data. This engine utilizes the vector store index as its underlying data source and enables us to query the Canada-specific information effectively. By incorporating the Canada engine into our AI agent's toolset, we can provide detailed and accurate responses to queries about Canada. The agent will intelligently choose the appropriate tool, whether it is the population data, the Canada PDF, or the note-saving functionality.
Final Remarks
In conclusion, building an artificial intelligence agent with diverse toolsets and data sources can greatly enhance its capabilities. By leveraging tools like Llama Index, Pandas, and OpenAI models, we can create AI agents that can retrieve and process information from various sources. Interacting with these agents allows us to issue queries, ask for data, and even save notes for future reference. The possibilities with AI agents are limitless, and this Tutorial has given us a glimpse into the power and potential of this technology.
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